Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy,
Challenges and Vision

Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

24 May 2022

Xiaolin Wang

Yingwei Luo

Papers citing "Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision"

6 / 6 papers shown

Title
FlexLLM: A System for Co-Serving Large Language Model Inference and Parameter-Efficient Finetuning Xupeng Miao Gabriele Oliaro Xinhao Cheng Vineeth Kada Ruohan Gao ... April Yang Yingcheng Wang Mengdi Wu Colin Unger Zhihao Jia MoE 94 9 0 29 Feb 2024
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm Yihong Li Xiaoxi Zhang Tian Zeng Jingpu Duan Chuanxi Wu Di Wu Xu Chen 21 15 0 01 Feb 2023
Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters Chen Sun Shenggui Li Jinyue Wang Jun Yu 54 47 0 08 Aug 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 150 674 0 24 Jan 2021
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges M. Netto R. Calheiros Eduardo Rodrigues R. L. F. Cunha Rajkumar Buyya 57 72 0 24 Oct 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 344 11,684 0 09 Mar 2017